J Chem Inf Model - Discovery of chemical compound groups with common structures by a network analysis approach (affinity prediction method).

Tópicos

{ compound(1573) activ(1297) structur(1058) }
{ network(2748) neural(1063) input(814) }
{ method(1969) cluster(1462) data(1082) }
{ extract(1171) text(1153) clinic(932) }
{ method(984) reconstruct(947) comput(926) }
{ perform(1367) use(1326) method(1137) }
{ first(2504) two(1366) second(1323) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ cancer(2502) breast(956) screen(824) }
{ sequenc(1873) structur(1644) protein(1328) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ motion(1329) object(1292) video(1091) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ clinic(1479) use(1117) guidelin(835) }
{ care(1570) inform(1187) nurs(1089) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ model(2341) predict(2261) use(1141) }
{ studi(1119) effect(1106) posit(819) }
{ record(1888) medic(1808) patient(1693) }
{ ehr(2073) health(1662) electron(1139) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
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{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
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{ blood(1257) pressur(1144) flow(957) }
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{ model(3480) simul(1196) paramet(876) }
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{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We developed a method in which the relationship between chemical compounds, characterized by the secondary dimensional descriptors by a standard method, is first determined by network inference, and then the inferred network is divided into the compound groups by network clustering. We applied this method to 279 active inhibitors of factor Xa found by the first screening. A large network of 266 active compounds connected with 408 edges emerged and was divided into 10 clusters. Surprisingly, the chemical structures that were common within the clusters, but diverse between them, could be extracted. The activity differences between the clusters provide rational clues for the systematic synthesis of derivatives in the lead optimization process, instead of empirical and intuitive inspections. Thus, our method for automatically grouping the chemical compounds by a network approach is useful to improve the efficiency of the drug discovery process.

Resumo Limpo

develop method relationship chemic compound character secondari dimension descriptor standard method first determin network infer infer network divid compound group network cluster appli method activ inhibitor factor xa found first screen larg network activ compound connect edg emerg divid cluster surpris chemic structur common within cluster divers extract activ differ cluster provid ration clue systemat synthesi deriv lead optim process instead empir intuit inspect thus method automat group chemic compound network approach use improv effici drug discoveri process

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